In telemedicine applications there is a need to share medical data between different sites. However, one problem in the industry is that some medical imaging applications require a large bandwidth to stream as a live video stream of medical images. Illustrative examples include live video streaming of ultrasound imaging, angiography, and endoscopy. Many types of medical images are difficult to efficiently compress. For example, ultrasound images have a high entropy content and have compression ratios that are dramatically lower than the compression ratios that can be achieved for streaming television and movies. An additional complication is that many small clinics have poor IT infrastructure with marginal and time-varying connections to the Internet, due to cost considerations, remote locations, or other reasons.
As an illustrative example of some of these problems, in ultrasound (u/s) imaging, image frames typically have a resolution of 512×512 pixels that are acquired at frame rates of 10 to 60 frames per second (fps). The frame rate may vary, depending in the u/s frequency of operation. The frame size is usually fixed, and the pixel resolution is again determined by the u/s frequency, and can vary from 400 microns at 2 MHz frequency to 80 microns at 10 MHz frequency. It is this pixel resolution that is relevant to the end-user, not the frame size itself. The frames at the frame rate are usually concatenated to form a standard video stream.
The stated frame size and rates mentioned above imply a raw data rate of 63 Mbps at 30 fps and 8 bits per pixel, gray scale. Without any compression, the data storage required to store one minute of the u/s stream at 30 fps and 8 bits per pixel is 450 MB. For color Doppler u/s imaging 12 bits per pixel is required, implying a raw data rate of about 95 Mbps at 30 fps and a data storage requirement to store one minute of the Doppler u/stream of 675 MB. Consequently, an image compression scheme has to be employed to reduce the bit rate for real-time network transport of the u/s scheme and to reduce the data storage requirements of the u/s scheme. However, the compression requirements for these two use cases need not be identical.
In medical imaging, lossless and lossy compression schemes are employed, depending on the imaging modality. Lossy compression may be employed as long as the quality of compressed images does not violate the Just Noticeable Difference (JND) threshold. This threshold is very subjective, but, many standards bodies such as the American College of Radiology (ACR) have established guidelines for compression rates for various medical imaging modalities.
Ultrasound images have a high entropy content and cannot be compressed with as high a compression ratio as conventional video streams for movies. For ultrasound imaging, many different compression standards are allowed. Examples of permissible compression standards for ultrasound imaging include Motion JPEG2000 (MJPEG2000), MPEG-4 and H.264.
Motion JPEG is a video codec in which each frame is separately compressed into a JPEG image. As a result the quality of the video compression is independent of the motion of the image. At low bandwidth availability priority is given to image resolution. In contrast, MPEG-4 is a standard that sends a reference frame and difference data for following frames (I frames, B frames, and P frames).
In the case of MJPEG2000, each image frame is compressed (either lossy or lossless) and every frame in the 30 fps stream is sent separately. Typical lossy compression rates (compression ratios) are of the order of 1:10 to 1:15. Further effective compression rates are not possible without losing image quality, since inter-frame data redundancy is not captured in MJPEG2000. Thus, while the compression rates of 1:10 to 1:15 rates of MJPEG2000 are good, there are problems in using MGPEG2000 for data storage or for network transport.
In the case of MPEG-4 and H.264, larger compression rates, from 1:20 through 1:80, are possible, since they utilize frame-to-frame redundancies and motion vector compensation schemes. For high entropy content images, such as ultrasound, the compression rates on the order of up to 1:20 to 1:40 are possible. These compression standards also allow for segmenting the images into multiple slices, and apply different compression rates for different schemes. An ultrasound image typically includes a main ultrasound image 105 (the active sub-image taken by the ultrasound probe) and border regions 110, 115 which may include labeling or text describing aspects of the image. Thus an image can often be segmented into strips. For the image example in FIG. 1, the image can be segmented into three strips—a top stripe having some textual data, a left stripe having textual data and the remaining sub-image which corresponds to the ultrasound image data.
The top and left stripes are highly compressible, to a few bytes, since inter-frame redundancies are very high. The active sub-image will have a lower compression rate, based on the mobility of the organ under exam.
Image data may be sent via a wired or wireless network. In a networked environment such as the internet, where these u/s streams are transported in real-time, there are dynamic conditions of the network that can momentarily constrict or disrupt the available bandwidth for u/s stream transport. As a result there can be a severe loss in the quality of the real time transaction and/or a loss in the connection, which is unacceptable in many applications.